Energy-Efficient NoC-Based Systems for Real-Time Multimedia Applications using Approximate Computing

• Wagner I. Penny UFPel / IFSUL http://orcid.org/0000-0003-2227-1218
• Daniel M. Palomino UFPel https://orcid.org/0000-0003-0409-8335
• Marcelo S. Porto UFPel https://orcid.org/0000-0003-3827-3023
• Bruno Zatt UFPel https://orcid.org/0000-0002-8045-957X

DOI: https://doi.org/10.5753/ctd.2021.15750

Resumo

This work presents an energy-efficient NoC-based system for real-time multimedia applications employing approximate computing. The proposed video processing system, called SApp-NoC, is efficient in both energy and quality (QoS), employing a scalable NoC architecture composed of processing elements designed to accelerate the HEVC Fractional Motion Estimation (FME). Two solutions are proposed: HSApp-NoC (Heuristc-based SApp-NoC), and MLSApp-NoC (Machine Learning-based SApp-NoC). When compared to a precise solution processing 4K videos at 120 fps, HSApp-NoC and MLSApp-NoC reduce about 48.19% and 31.81% the energy consumption, at small quality reduction of 2.74% and 1.09%, respectively. Furthermore, a set of schedulability analysis is also proposed in order to guarantee the meeting of timing constraints at typical workload scenarios.

Palavras-chave: NoC, Approximate Computing, Machine Learning, Video Coding, Hardware Acceleration

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